The use of vibration analysis as part of maintaining rotational mechanical systems is well known. Diagnostics of mechanical systems using vibration signatures have been researched in academic frameworks in connection with gear diagnostics, helicopter diagnostics, robots, ship vibrations, tool-ware monitoring, and transportation. Another application is power plant monitoring systems, especially those used in nuclear plants, which frequently use vibration signatures to identify worn parts requiring maintenance and other faults.
In an aircraft engine, each machine (combination of parts applying energy to do work), such as fan, compressor, turbine, and gear box, has a unique and repeatable vibration signature. Because the levels, profile, and features of these vibration signatures correlate well between runs of the same engine, as well as between different engines of the same type, vibration signatures can be a useful diagnostic tool. The high correlation between the levels, profile, and features of each run, for each machine mentioned above, can be seen in FIG. 1, which illustrates vibration signatures from two runs for an exemplary engine, and in FIG. 2, which illustrates the similarity in levels, profile, and features of vibration signatures of different engines of the same type. FIG. 1 illustrates frequency domain analysis data from two runs on the same machine. As one of ordinary skill in the art will appreciate, the data collected during the runs is substantially identical. The levels and locations of peaks are almost the same between the two runs. FIG. 2 illustrates frequency domain analysis data from two runs on two engines of the same type. As may be appreciated, the collected data is closely correlated between the two runs.
Generally, monitoring a mechanical system by analyzing vibration signatures begins with collecting vibration data at various points in the system using vibration sensors. The data is analyzed manually, electronically, or by a combination of the two, to determine whether the data reflects normal or abnormal conditions of the mechanical system. The vibrations represent the structural, dynamic, and aerodynamic characteristics of the observed components. In this manner, abnormalities, such as cracks, deformities, defective parts, and deteriorated engine modules may be diagnosed and the necessary maintenance may be performed.
In addition to using vibration signatures for diagnostics, recent attention has been given to the use of vibration data for trend analysis, or prognostics, in mechanical systems. Trend analysis is generally concerned with identifying an abnormality at its incipient stage. Trend analysis is a valuable tool, which enables one to proceed with corrective steps before an abnormality grows to a more costly, or even catastrophic, condition.
Diagnostic and prognostic approaches to mechanical systems using vibration signatures have continually progressed. For example, recent approaches include expanded automation, so as to significantly reduce dependence on a human operator. Moreover, while earlier approaches required shutting down operations in order to install a diagnostic apparatus, take measurements, and perform the necessary analysis, more recent systems have been designed to allow for online data collection. Finally, the tools for analyzing the data to reach a diagnosis or prognosis have become more sophisticated, and therefore more sensitive to abnormalities and trend data.
However, current systems do not allow for concurrent data collection and data processing. Furthermore, the utilized vibration analysis is usually limited to narrow bandwidth spectrum and to a single domain, such as the frequency domain. In other words, the vibration signal is represented as a function over a set of frequencies. Typically the Fast Fourier Transform (“FFT”), is used to provide the representation of the vibrational signature in the frequency domain. However, the FFT, because it is based on a single frame of data with a statistical error measured as 1/(Number of frames), is statistically unreliable. Even recent applications that have turned to the power spectral density (“PSD”) for vibration analysis, because it provides higher reliability than the FFT, are generally limited to a single domain. Such FFTs are discussed in A. Mertins, Signal Analysis—Wavelets, Filter Banks, Time-Frequency Transforms and Applications (John Wiley & Sons, 1999), hereby incorporated by reference as if fully set forth herein.
Some applications calculate the PSD using Auto Regressive Moving Average (“ARMA”) modeling. ARMA modeling is used to detect structural frequencies of the machinery as a rigid body and structural frequencies of its constituent parts. The vibrations spectrum/spectrogram is estimated using ARMA model parameters. It is known that the spectrum obtained with ARMA modeling emphasizes the structural frequencies better than the FFT based spectra. A spectrum estimated using ARMA modeling is equivalent, with respect to the signal to noise ratio (“SNR”) of the result, to an average of 1000 frames in the traditional FFT-based PSD. ARMA modeling is also described in A. Mertins, Signal Analysis—Wavelets, Filter Banks, Time-Frequency Transforms and Applications.